Preventing unintended proteolysis in plant protein biofactories
Bibliographic record
Abstract
SUMMARY: Numerous reports have been published over the last decade assessing the potential of plants as useful hosts for the heterologous expression of clinically useful proteins. Significant progress has been made, in particular, in optimizing transgene transcription and translation in plants, and in elucidating the complex post-translational modifications of proteins typical of the plant cell machinery. In this article, we address the important issue of recombinant protein degradation in plant expression platforms, which directly impacts on the final yield, homogeneity and overall quality of the resulting protein product. Unlike several more stable and structurally less complex pharmaceuticals, recombinant proteins present a natural tendency to structural heterogeneity, resulting in part from the inherent instability of polypeptide chains expressed in heterologous environments. Proteolytic processing, notably, may dramatically alter the structural integrity and overall accumulation of recombinant proteins in plant expression systems, both in planta during expression and ex planta after extraction. In this article, we describe the current strategies proposed to minimize protein hydrolysis in plant protein factories, including organ-specific transgene expression, organelle-specific protein targeting, the grafting of stabilizing protein domains to labile proteins, protein secretion in natural fluids and the co-expression of companion protease inhibitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".